Transcriptomics
Training material for all kinds of transcriptomics analysis.
Requirements
Before diving into this topic, we recommend you to have a look at:
- Introduction to Galaxy Analyses
- slides Slides: Quality Control
- tutorial Hands-on: Quality Control
- slides Slides: Mapping
- tutorial Hands-on: Mapping
Material
You can view the tutorial materials in different languages by clicking the dropdown icon next to the slides (slides) and tutorial (tutorial) buttons below.Introduction
Start here if you are new to RNA-Seq analysis in Galaxy
| Lesson | Slides | Hands-on | Recordings | Input dataset | Workflows |
|---|---|---|---|---|---|
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Introduction to Transcriptomics
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| Reference-based RNA-Seq data analysis | |||||
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De novo transcriptome reconstruction with RNA-Seq
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End-to-End Analysis
These tutorials take you from raw sequencing reads to pathway analysis
| Lesson | Slides | Hands-on | Recordings | Input dataset | Workflows |
|---|---|---|---|---|---|
| 1: RNA-Seq reads to counts | |||||
| 2: RNA-seq counts to genes | |||||
| 3: RNA-seq genes to pathways |
Visualisation
Tutorials covering data visualisation
Other
Assorted Tutorials
Frequently Asked Questions
Common questions regarding this topic have been collected on a dedicated FAQ page . Common questions related to specific tutorials can be accessed from the tutorials themselves.
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Community Resources
Community Home Maintainer HomeEditorial Board
This material is reviewed by our Editorial Board:
Bérénice Batut
Maria Doyle
Florian Heyl
Pavankumar Videm
Are you on the editorial board or want to help out with this topic? View the topic maintainer page for ways to improve this topic!
Contributors
This material was contributed to by:
Toby Hodges
Charlotte Adang
Sanjay Kumar Srikakulam
Clea Siguret
Mehmet Tekman
Mo Heydarian
Anthony Bretaudeau
Anton Nekrutenko
Cristóbal Gallardo
Niall Beard
Chao Zhang
Mallory Freeberg
Mateusz Kuzak
Clemens Blank
Anne Fouilloux
Harriet Dashnow
Matti Hoch
Charity Law
Beatriz Serrano-Solano
Peter van Heusden
Olivier Dameron
Nicola Soranzo
Martin Čech
Saskia Hiltemann
Anna Trigos
Xavier Garnier
Myrthe van Baardwijk
Daniel Maticzka
Lucille Delisle
James Taylor
Belinda Phipson
Sofoklis Keisaris
Wolfgang Maier
Tristan Reynolds
Linelle Abueg
Anne Siegel
Björn Grüning
Hans-Rudolf Hotz
Helena Rasche
Pavankumar Videm
Casper de Visser
Simon Bray
Iacopo Cristoferi
Bérénice Batut
Xi Liu
Mira Kuntz
Ekaterina Polkh
Maria Doyle
Amirhossein Naghsh Nilchi
Andrea Bagnacani
Jovana Maksimovic
William Durand
Peter-Bram 't Hoen
Matt Ritchie
Florian Heyl
Teresa Müller
Erwan Corre
Marek Ostaszewski
Armin Dadras
José Manuel Domínguez
Mateo Boudet
Anika Erxleben
Fotis E. Psomopoulos
Markus Wolfien
Gildas Le Corguillé
Graeme Tyson
Shian Su
Marius van den Beek
IGC Bioinformatics Unit
Funding
These individuals or organisations provided funding support for the development of this resource
BeYond-COVID
BY-COVID is an EC funded project that tackles the data challenges that can hinder effective pandemic response.
This project has received funding from the European Union’s Horizon Europe research and innovation programme under grant agreement № 101046203 (BY-COVID)
Melbourne Bioinformatics
Australian BioCommons
The University of Melbourne
de.NBI
Gallantries
This project (2020-1-NL01-KA203-064717) is funded with the support of the Erasmus+ programme of the European Union. Their funding has supported a large number of tutorials within the GTN across a wide array of topics.
References
- Shirley Pepke et al: Computation for ChIP-seq and RNA-seq studies
-
Paul L. Auer & R. W. Doerge: Statistical Design and Analysis of RNA Sequencing Data
Insights into proper planning of your RNA-seq run! To read before any RNA-seq experiment! -
Ian Korf: Genomics: the state of the art in RNA-seq analysis
A refreshingly honest view on the non-trivial aspects of RNA-seq analysis -
Marie-Agnès Dillies et al: A comprehensive evaluation of normalization methods for Illumina high-throughput RNA sequencing data analysis
Systematic comparison of seven representative normalization methods for the differential analysis of RNA-seq data (Total Count, Upper Quartile, Median (Med), DESeq, edgeR, Quantile and Reads Per Kilobase per Million mapped reads (RPKM) normalization) -
Franck Rapaport et al: Comprehensive evaluation of differential gene expression analysis methods for RNA-seq data
Evaluation of methods for differential gene expression analysis - Charlotte Soneson & Mauro Delorenzi: A comparison of methods for differential expression analysis of RNA-seq data
- Adam Roberts et al: Improving RNA-Seq expression estimates by correcting for fragment bias
-
Manuel Garber et al: Computational methods for transcriptome annotation and quantification using RNA-seq
Classical paper about the computational aspects of RNA-seq data analysis - Cole Trapnell et al: Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks
- Zhong Wang et al: RNA-Seq: a revolutionary tool for transcriptomics
- Dittrich, M. T. and Klau, G. W. and Rosenwald, A. and Dandekar, T. and Muller, T.: Identifying functional modules in protein-protein interaction networks: an integrated exact approach
- May, Ali; Brandt, Bernd W; El-Kebir, Mohammed; Klau, Gunnar W; Zaura, Egija; Crielaard, Wim; Heringa, Jaap; Abeln, Sanne: metaModules identifies key functional subnetworks in microbiome-related disease
- Pavankumar, Videm; Dominic, Rose; Fabrizio, Costa; Rolf, Backofen: BlockClust: efficient clustering and classification of non-coding RNAs from short read RNA-seq profiles